Next: Examples
Up: Interpolation algorithm
Previous: Triangulation
After finding the simplexes, we need to interpolate in their interiors. In our
current implementation, we considered a linear variation between the
vertices that would ensure that the resulting surface is continuous at the
boundaries, though it would not have a continuous derivative across. Other
options we considered are to use polynomial fits or cubic-spline functions, but
only if we need increased precision that cannot be achieved otherwise, due to
the extra computational cost. The latter options would provide a surface that is
both continuous and has a continuous derivative across the triangle boundaries.
The final question we had to answer is which points of the grid fall inside the
simplex. We considered two possible answers:
- 1.
- Find a domain in the rectangular grid that completely bounds each
simplex, and search for all the points of the rectangular grid that fall
inside the analyzed simplex. This is obviously not the best choice
because we have to analyze more than the necessary number of points (twice
as many in the 2-D case and three times as many in the 3-D case). The number
of operations is proportional both to the number of triangles and to the size
of the interpolation grid.
- 2.
- Search for the limits of the intersection of each simplex with the
rectangular grid and consider all the points inside. Such an algorithm can
be implemented fairly easily and is a lot faster than the preceding one.
The number of operations is proportional to the number of triangles
and does not depend on the size of the grid. This is a way to build
the optimal solution and avoid a blind search.
Next: Examples
Up: Interpolation algorithm
Previous: Triangulation
Stanford Exploration Project
10/9/1997